Quantum Coherence Applications in Modular Computing

Check out the latest from MIT EQuS and Lincoln Laboratory published in @NaturePhysics! In this work, we demonstrate a quantum interconnect using a waveguide to connect two superconducting, multi-qubit modules located in separate microwave packages. We emit and absorb microwave photons on demand and in a chosen direction between these modules using quantum entanglement and quantum interference. To optimize the emission and absorption protocol, we use a reinforcement learning algorithm to shape the photon for maximal absorption efficiency, exceeding 60% in both directions. By halting the emission process halfway through its duration, we generate remote entanglement between modules in the form of a four-qubit W state with concurrence exceeding 60%. This quantum network architecture enables all-to-all connectivity between non-local processors for modular, distributed, and extensible quantum computation. Read the full paper here: https://lnkd.in/eN4MagvU (paywall), view-only link https://rdcu.be/eeuBF, or arXiv https://lnkd.in/ez3Xz7KT. See also the related MIT News article: https://lnkd.in/e_4pv8cs. Congratulations Aziza Almanakly, Beatriz Yankelevich, and all co-authors with the MIT EQuS Group and MIT Lincoln Laboratory! Massachusetts Institute of Technology, MIT Center for Quantum Engineering, MIT EECS, MIT Department of Physics, MIT School of Engineering, MIT School of Science, Research Laboratory of Electronics at MIT, MIT Lincoln Laboratory, MIT xPRO, Will Oliver

IBM Successfully Links Two Quantum Chips to Operate as a Single Device Key Insights: • IBM has achieved a significant milestone by linking two quantum chips to function as a single, cohesive system, enabling them to perform calculations beyond the capability of either chip independently. • This accomplishment supports IBM’s modular approach to building scalable quantum computers, a strategy aimed at overcoming the limitations of single-chip architectures. • The linked chips demonstrated successful cooperation, marking a step closer to larger and more powerful quantum systems capable of addressing complex real-world problems. The Modular Quantum Computing Approach: • IBM employs superconducting quantum chips, manufactured using processes similar to traditional semiconductor technology, allowing scalability and integration with existing hardware infrastructure. • Modular quantum systems involve linking smaller quantum processors, rather than relying on a single massive chip, reducing fabrication challenges and improving scalability. • This architecture allows multiple chips to share quantum information seamlessly, paving the way for constructing larger quantum systems without exponentially increasing hardware complexity. Addressing Key Challenges in Quantum Computing: • Scalability: Connecting multiple chips is a critical step toward scaling quantum computers to thousands or even millions of qubits. • Error Reduction: Larger quantum systems increase susceptibility to errors. Modular architectures provide pathways for better error management and correction across linked processors. • Coherence Across Chips: Maintaining the delicate quantum states across separate chips is technically challenging, and IBM’s success suggests progress in solving this issue. Implications of IBM’s Achievement: • Enhanced Computational Power: Linked quantum chips unlock the potential for more complex simulations and problem-solving capabilities. • Practical Quantum Applications: Industries like pharmaceuticals, cryptography, and materials science may soon benefit from more robust and scalable quantum computing solutions. • Competitive Advantage: IBM’s progress underscores its leadership in modular quantum computing, positioning it strongly in the competitive quantum technology landscape. Future Outlook: IBM’s successful demonstration of inter-chip quantum communication validates the modular quantum computing strategy as a viable path to scaling up systems. Future advancements will likely focus on enhancing chip-to-chip communication fidelity, increasing the number of interconnected chips, and reducing overall error rates. This breakthrough brings us one step closer to practical, large-scale quantum computing systems capable of solving problems previously deemed unsolvable by classical computers.

Delighted to share some work we've been developing over the past months! 📄 🔗🔗 https://lnkd.in/enXEQdDc 🔗🔗 ✨ Building on our previous research [https://lnkd.in/eCjCDv2D], we've explored a new direction for modular quantum computing with surface codes. The focus is on whether emission-based hardware can support fault-tolerant quantum error correction. The question we set out to answer: 🤔 📡 Can we distribute entanglement across modules without relying on slow and noisy two-qubit gates? 🔗 Our earlier work showed emission-based platforms were feasible but limited to thresholds of 0.16 % ⚡ Is there a more efficient protocol path forward? Our approach: 🎯 We propose single-shot GHZ state generation — creating the entangled states needed for stabilizer measurements directly, without Bell-pair fusion. The optical setup generates Bell pairs, W states, and GHZ states by simply observing photon detection patterns. Benchmarking on realistic hardware: 🧪 #DiamondColorCenters #QuantumHardware 🔴 We modeled this for diamond color-center platforms (what experimentalists are actually building) 🔴 Full noise modeling includes photon loss, detector efficiency, and circuit-level errors 🔴 Both photon-number-resolving and standard detectors analyzed The findings: 📊 We're grateful for what the analysis reveals about this architecture with circuit-level noise: 💎 Threshold of 0.24 % with photon-number-resolving detectors 💎 Threshold of 0.19 % with standard detectors 💎 These thresholds scale with hardware improvements — unlike previous approaches that saturated Why this matters: 🛣️ #FaultTolerance #ModularQuantumComputing #QuantumErrorCorrection This work suggests a practical pathway toward scalable modular quantum computers using hardware that's already being developed in labs. The protocols require only modest enhancements to existing emission-based setups. Looking ahead: 🔮 #ExperimentalQuantum #QuantumNetworks #DistributedQuantum We hope these results help guide the experimental community's next steps. We've tried to provide clear hardware targets and realistic thresholds that could inform near-term implementations. Special thanks to our collaborators at QuTech, Keio University, and OIST for making this collaborative effort possible. 🙏 Daniel Bhatti, Rikiya Kashiwagi, David Elkouss, Kazufumi Tanji, Wojciech Roga, Masahiro Takeoka #QuantumComputing #SurfaceCode #Photonics #ColorCenters #QuantumErrorCorrection #ModularArchitectures #QuantumInternet

⚛️ EFFICIENT AND SCALABLE INTER-MODULE SWITCHING FOR DISTRIBUTED QUANTUM COMPUTING ARCHITECTURES 📑 Large-scale fault-tolerant quantum computers of the future will likely be modular by necessity or by design. Modularity is inevitable if the substrate cannot support the desired error-correction code due to its planar geometry or manufacturing constraints resulting in a limited number of logical qubits per module. Even if the computer is compact enough there may be functional requirements to distribute the quantum computation substrate over distant regions of varying scales. In both cases, matter-based quantum information, such as spins, ions or neutral atoms, is the most conveniently transmitted or mediated by photonic interconnects. To avoid long algorithm execution times and reduce errors, each module of a universal quantum computer should be dynamically interconnected with as many other modules as possible. This task relies on an optical switching network providing any-to-any or sufficiently high simultaneous connectivity. In this work we construct several novel and decentralized switching schemes based on the properties of the Generalized Mach-Zehnder Interferometer (GMZI) that are more economic and less noisy compared to commonly considered alternatives while achieving the same functionality. ℹ️ K. Brádler - 2025

🔴 Xanadu publishes a milestone in #Nature. The paper Scaling and networking a modular photonic quantum computer proves that the path to millions of #qubits isn't making a bigger chip. It's networking them together. Building a monolithic #QuantumProcessor is hitting a yield and size wall. To scale, we must go #Modular. This work demonstrates a programmable, distributed quantum system that connects distinct #QuantumModules via #OpticalFibers, effectively turning a room full of server racks into a single giant quantum processor. 🔴 1. The Aurora Architecture The team unveiled a system comprising three interconnected quantum modules. Unlike #SuperconductingQubits which require complex microwave-to-optical transducers to leave the fridge, #PhotonicQubits are light. This allows for native, low-loss communication between modules using standard optical fibers, enabling a true #DataCenterScale quantum system. 🔴 2. Beating the #PercolationThreshold Connecting chips is easy, maintaining #entanglement across them is hard. The crucial breakthrough here is achieving an inter-module connection quality that exceeds the Percolation Threshold for #FaultTolerance. This means the distributed #ClusterState is robust enough to support #QuantumErrorCorrection, proving that modularity does not compromise computational reliability. 🔴 3. Synthetic Dimensions via #TimeMultiplexing Instead of just printing more physical qubits, Xanadu leverages Time-Domain Multiplexing (#TDM). They generate streams of entangled #SqueezedLight pulses that form a 3D cluster state in time. This allows a compact hardware footprint to generate a massive, scalable resource state for Measurement-Based Quantum Computing (#MBQC). 👇 Link in the comments #QuantumTech #Photonics #SiliconPhotonics #QuantumNetwork #QuantumInformation #OpticalInterconnect #AdvancedPackaging #Chiplet #MooreLaw #MoreThanMoore #SignalIntegrity #HardwareArchitecture #Semiconductor #Optoelectronics #HeterogeneousIntegration #Telecommunications #DataCenter PsiQuantum IonQ Rigetti Computing IBM Quantum Google Quantinuum D-Wave Intel Corporation TSMC Samsung Electronics SK hynix NVIDIA AMD Broadcom Marvell Technology Cisco GlobalFoundries Applied Materials Corning Incorporated